Interactive Plotting with Plotly

Interactive Plotting with Plotly#

Why Use Plotly?#

Most plots you’ve made so far (with matplotlib or seaborn) are static — once you export them, you can’t zoom, hover, or explore them.

Plotly lets you create interactive plots where you can:

  • Hover over data points for values

  • Zoom, pan, and export plots

  • Highlight experimental trends visually

  • Create web-based reports or dashboards

Built-In Tools#

All Plotly plots include:

  • Zoom and pan

  • Hover for exact values

  • Export to PNG or SVG

  • Reset view button

You can also save a plot to HTML:

fig.write_html("my_interactive_plot.html")

Use Cases in Biochemistry:#

  • Explore time-course data (e.g., enzyme kinetics)

  • Visualize sample comparisons interactively

  • Build dashboards for high-throughput data

Plotly works well in Jupyter Notebooks and can also export to HTML or PNG.

Let’s start with a simple enzyme activity example!

# You may need to install plotly first (only once):
!pip install plotly

import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
#notebook renderer
import plotly.io as pio
pio.renderers.default = 'notebook'

Requirement already satisfied: plotly in c:\users\benhuang\anaconda3\envs\data-book\lib\site-packages (6.2.0)
Requirement already satisfied: narwhals>=1.15.1 in c:\users\benhuang\anaconda3\envs\data-book\lib\site-packages (from plotly) (1.44.0)
Requirement already satisfied: packaging in c:\users\benhuang\appdata\roaming\python\python313\site-packages (from plotly) (25.0)

Interactive Line Plot: Enzyme Activity#

You measured enzyme activity every 5 minutes. Let’s plot the data interactively.

# Sample data
time_min = [0, 5, 10, 15, 20, 25, 30]
activity = [0.0, 1.9, 3.8, 5.3, 6.8, 7.5, 8.0]

# Create a line plot
fig = go.Figure()

fig.add_trace(go.Scatter(
    x=time_min,
    y=activity,
    mode='lines+markers',
    name='Enzyme Activity',
    line=dict(color='blue'),
    marker=dict(size=8)
))

# Add layout
fig.update_layout(
    title='Enzyme Activity Over Time',
    xaxis_title='Time (minutes)',
    yaxis_title='Activity (µmol/min)',
    template='plotly_white'
)

fig.show()

Bar Chart: Comparing Treatment Groups#

You ran an ELISA and want to compare protein levels in 3 groups interactively.

# Create a sample dataset
df = pd.DataFrame({
    'Group': ['Control', 'Treated A', 'Treated B'],
    'Protein (µg/mL)': [1.2, 2.6, 2.1]
})

# Use plotly express for simple bar plot
fig = px.bar(df, x='Group', y='Protein (µg/mL)',
             color='Group',
             title='Protein Concentration by Group',
             text='Protein (µg/mL)')

fig.update_traces(textposition='outside')
fig.update_layout(template='plotly_white')
fig.show()

Summary#

In this chapter, you learned how to:

  • Create interactive plots with plotly

  • Visualize time-course data (enzyme activity)

  • Compare treatment groups using interactive bar charts

  • Export your plots to HTML or PNG

Plotly is perfect for building interactive dashboards or exploring large experimental datasets.

You can use it alongside matplotlib or seaborn — depending on whether you need publication-ready figures or interactive exploration.

Contents